On the Chicago Cubs, Draft Inefficiencies, and the Last Mover’s Advantage

There’s a scene in Moneyball where Red Sox owner John Henry offers his vacant GM position to Billy Beane. “Anybody who’s not tearing their team down right now and rebuilding it using your model,” he says. “They’re dinosaurs.” The Oakland A’s were the first movers of modern sports analytics. They took a risk, and while there were stumbles along the way, they benefitted as a result. In hockey, it took a decade longer for any kind of true analytic implementation, and we’re still not quite in “tear down and rebuild using your model” territory. So why has it taken so long? I think the answer lies in the hockey world’s view of baseball. NHL executives are drawn to the differences between the two sports rather than their similarities. Yes, baseball is a stop-start game whereas hockey is fluid. And yes, baseball involves more one-on-one matchups and less team play. But beyond that, the games – and the strategies that result in building the best possible teams – are actually quite similar.

Why does this matter? Well I think that until recently, NHL teams have been afraid to commit to analytics as an organizational approach – and to admit to conducting analytical work to the media at all – through fear of being the first mover. Executives were afraid that if they were to, say, hire a blogger to their front office, or evaluate players largely based on statistical trends and evaluations, they’d be out of a job if results didn’t go their way. But while such an executive might be a first mover in hockey, he wouldn’t be in the grand scheme of things. Analytical approaches work, and baseball has proven that. So what the first NHL GM to truly buy into analytics had was the advantage of being a last mover in the sports industry – avoiding early mistakes and being able to build on other models – while avoiding the disadvantages that generally go along with arriving late to the party because hockey teams specifically (the main competitors) were all just as far behind. The first GM to seriously adopt analytics in hockey was poised to have the best of both worlds, but because most saw baseball as just a stop-and-start game of catch, it took years for anybody to take the plunge.

I make this comparison between hockey and baseball because baseball writer and sabermetrician Rany Jazayerli wrote a piece at Grantland a couple of days ago about the Chicago Cubs’ rebuilding efforts. The Cubs, now run by former Red Sox GM Theo Epstein – aka the guy who got the job Beane turned down – are using analytics to exploit a rather basic market inefficiency. They found that drafting and developing pitchers was a far riskier proposition than doing so with hitters, and delivered less value. So they just stopped doing it.

Not completely, of course. They didn’t go full Sham Sharron and hit the beach while letting algorithms decide their picks, but with the luxury of four consecutive top-10 selections, and ignoring the conventional baseball wisdom that hard-throwing young pitchers are critical to success and therefore the ideal top draft picks, they took hitter after hitter after hitter after hitter. And while a number of the pitching prospects they were supposed to take have faded whether as the result of injury or lost control, the Cubs’ hitters are thriving and look poised to take the major leagues by storm.

But what about pitching? Well rather than bearing the risk of developing young pitchers, the Cubs have waited and pounced later in the process. Earlier this year, for example, they acquired a pitcher by the name of Jacob Turner on waivers from the Florida Marlins. Turner’s stock had fallen considerably (since being the main prospect in a package the Marlins acquired from the Tigers) as a result of his earned run average jumping from 3.74 to 5.94. But statistical analysis in baseball has revealed that a pitcher only really has control over walks, strikeouts, and home runs, and that therefore fielding-independent pitching (FIP) is a better judge of pitching talent. Turner’s FIP had, in the same short time period, improved from 4.43 to 4.00. He had suffered bad luck as a result of his opposition’s hit location and the fielding behind him, and the Cubs were able to grab a pitcher poised for a bounce-back to complement their stable of young hitters. Rinse and repeat.

In hockey, hit location and fielding are essentially congruent with shot location and goaltending. In our sport, analysts have found that defensemen have very little control over either team’s save percentage while they’re on the ice, and that therefore the best method we have for evaluating them at this point is using Corsi% – in unison of course with contextual factors like offensive zone start percentage and quality of teammates. Corsi is essentially our FIP, and while baseball and hockey may be vastly different sports, the approach the Cubs have taken to rebuilding is one that could be replicated in hockey quite easily. Here is a chart of young defensemen who have either been linked to trades or could potentially have been available in the near past.

Some of these guys were available based on low PDOs that could have largely been the result of variance, some simply didn’t fit the mold of defensemen coaches were looking for, and some may not have been on the block at all, but for the right package might have been had. And this is only a very brief list of the most obvious and enticing candidates.

One of the largest market inefficiencies in pro hockey is an under-appreciation of young, puck-moving defensemen. Some of these guys could have been acquired, and teams that had spent draft picks primarily on forwards – hockey’s draft equivalent of baseball’s hitters – likely would have had the resources to get them, rather than wasting picks on guys like Alex Plante, Keaton Ellerby, Dylan McIlrath and Tyler Cuma.

To bring in yet another sport, Sacramento Kings owner Vivek Ranadive recently stated that ” In the 21st century, math will trump science…you don’t need to know the why, you simply need to find the pattern”. While I would disagree that this is always the case – we can be fooled by flawed analysis if we don’t understand the science behind it – with strict scrutiny he is essentially correct. It doesn’t matter why forwards are easier to forecast than defensemen and goalies, and it doesn’t even matter if the strategy is generalized to the extent that teams might miss out on certain star defensemen the scouts dub sure things. On a macro scale, drafting mostly forwards and then exploiting trade inefficiencies on young defensemen and goalies is a strategy that would bring value. The math is there to prove it.

An NHL team that decided to go in this direction would have a massive competitive advantage – although with the recent hires it’s shrinking every day – they would have first mover’s advantage in an industry in which the method is already tried and on the verge of being proven true.

If hockey executives spent a little bit less time downplaying the relevance of baseball’s findings and little bit more time learning from them, they could change the game for the better. They could innovate. And in doing so, they could build a winner. Analytics is about information, adaptation, and innovation. It’s time for the dinosaurs of hockey to head John Henry’s warning. Tear down and rebuild. Learn from the tried and true.